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Introduction

In the real estate industry, increasing population projects a progressive opportunity in estimating future potential demand on dwellings. The significant increase in global ageing population has been recognised for decade in connection with the trends in morbidity and mortality of older adults has continuously dropped. So does in Thailand, referring to the annual publications of National Committee for the Elderly since 2009, it has always been highlighted that Thai ageing population whose aged 60 years old and above has been statistically striking up (Siriphanich, 2017).

The United Nations also estimate the total number of Thai populations aged 60 and above that by 2050 the number will reach 20,961,000 from the current number at 13,412,000. (AgeingAsia, 2019); (Vichit-Vadakan et al., 2002); (United Nations, 2019). The aforementioned annual report also added that Thailand enters to ageing society very much faster than other developed countries in the world.

This is due to, according to the report, an astonishing success of the Birth control campaign to the extent that Thai people have a longer life expectancy. Healthcare is also being concluded as one of the reasons that significantly proven contributing to the increase of population. In 2018, Keith Pollarda CEO of LaingBuisson International Limited, a web publishing business in the healthcare sectorranked Thailand as one of the best Medical Travel destinations for the global healthcare quality and safety (Pollard, 2018). The extend of our advanced medical technology and facilities with licensed and accredited medical professionals while the costscomparing to other countries where offer an equivalent serviceare much lower.

In the meantime, Bangkok has been expected to become Smart cities (Krasaesan, 2016) in which the city must be in accordance with the International standards as ideally projects the safety, reliability and compatibility of diverse technologies. The residents shall be fully aware of the united objective to the extent of its righteous and in a balance aspect of economics and technology.

Provided which, Internet of Things (IoT) has proven its innovation that its utilities are practically assistive to ageing users to their patterns of life and not limited to living at home and IoT will be even more efficient when each device is connected to each other and constantly collect and exchange data. Technology can ensure ones Quality of Life regardless of the place it is presently in use. Nowadays, IoT devices have seamlessly become a great element of our a-day-in-a-life.

Inside our house or outdoor, in a building or on all cars, there is at least one object that has been embedded in. Thus, this research believes that by allowing them to be one of the accountable components for assisting the elderly with any activities, starting at their own house, such balance between technology and senior dwelling shall improve the idea of further better livings where meant not only for seniors but to be all ages-friendly environments for the whole family to live soundly and conveniently with assistive technology together.

While healthy ageing is one of the concepts that being promoted in order to positively urge the attention of the society to of quality of life, this research explores an approach to a dwelling where efficiently produces such optimistic to not only the elderly but ideally their family members with the ideology of dwelling elements where promotes a smart way of living that embrace the independence in the essence given the age-related sensory changes context-oriented in its core design to prosperous to Ageing Society Thailand has entered in.

In Thailand, the investors come up with different techniques used in the design of specialty and services mechanism for seniors assisted living. However, given the complexity of being age and being a part of a society, these projects are either too specificgiven the chronic conditions and variety of diseases in ageing people or too generalwhere nothing is rarely different except the label due to the developers feasibility matter. Hence key factors to a successful senior living project in Thailand are

1. In medical terms: A full range of medical facilities and certified professional services with a well-trained team member;
2. In project development terms: A suitable well-design building and environment specifically supporting older adults. Including location aspect its surrounding area that should not be constrains to the elderly to access and live in peacefully; and
3. Creditability of both major partiesWell-reputative medical facilities centre and a well-recognised real estate developer, for the Quality of Life is eventually considered very essential for the longevity of life.

Partnership definitely makes it possible and strong considered from how some of the successful senior living projects in Thailand came from. Senior livings comprise of some specialty essences, this therefore brings about risks to developers who may possess some limitations in regards of the Time, Cost, Quality concerns for one project as a unit price in these projects is considerably high regardless of the technology chosen to integrated for the residencys ideal smart quality of life. This thus is affordable to only some groups of families in a society, and mortgage for senior citizen is understandably aggressive such as the loan amount is only all-inclusive 60% granted while the asset price must not be lower than 1.5m baht and the loan term is limited and up to 85 years old.

This results in how the project that might have a well combination of technology and the least basic ageing people-oriented strategy in its core design of a senior living in the market in Thailand is yet to be a true ideal project that meant truly for the elderly in Thailand. And to date, there is no research with an ideology of technology-integrated ageing people-oriented livings.

Thailand as in Aging society. Our country has already become ageing society since year 2003 according to the National Committee for the ElderlyFoundation of Thai Gerontology Research and Development Institutewho was assigned to stipulating the preparation of an annual report on the status of the elderly for submission to the Cabinet accordance with the Elderly Act of 2003 (Siriphanich, 2017).

Term of Aging SocietyReferring to the definitions of Aging society announced from the United Nations in 2006, the terms are grouped as follows: Aging Society  The society where the ratio of population aged over 60 years old is over 10% of its total population, or population aged over 65 years old is over 7% of its total population. (Byrnes et al., 2006) Aged Society  The society where at least 20% of its total population aged over 60 years old; or 14% of its total population aged over 60 years old. (Luken & Vaughan, 2003) Super-Aged Society  The society where its total population aged over 65 years is equivalent to or higher than 20% of total population (Siriphanich, 2017).

Age Range Category of ElderlyGroups older adults by decade of age that are relative to data on the national statistics in older adults, so we use the Sexagenarian (60-69 years old); Septuagenarian (70-79 years old); Octogenarian (80-89 years old) (Carr, 2019); (Hebert, 2010); (Roestorf et al., 2019).

Average monthly income per household in Bangkok ThailandAccording to the Department of Elderly Affair in Thailand (2019), in Bangkok, the total number of populations is 5,676,648 persons. In this number, those senior persons who age 60 years old and above are 1,020,917 persons. This equals 17.98% from the total population. Additionally, The National Statistical Office of Thailand (2019), the total is amounted to approximately 43,286.85 and 43,802.19 Thai baht in 2016-2017 respectively.

Objectives

• Use environmental gerontology to explore how IoT and smart home technology can aid the elderly in their day to day activities at home. This will help understand the needs of the older adults in relation to the support that smart home technology can provide.
• To explore the actual impact of smart home technology on elderly peoples living and how it aids them in their daily lives and to create a list of technology that is preferenced by the Thai older adults.
• To explore the topic of smart dwelling pertaining to the senior population to outline the principles of integrated assistive technology that can be used when creating devices or designing for assisted living. The intention is to help real estate companies and technology startups understand the preferences of the elderly in relation to smart living and the circumstances under which they choose to purchase these devices.

Materials and Methods

This is a mixed-method study, where qualitative and quantitative data is analyzed to determine the behavioural patterns of the Thai elderly who use smart technology. The data was collected through interviews and survey questions to facilitate the behavioural analysis, which is used in research to understand how different factors impact human behaviour and how this knowledge can be used by practitioners (Luiselli, 2017). A combination of an interview and survey was chosen as an alternative to direct observation, to collect quantitative data for the detection of patterns and qualitative information from the face-to-face interactions with the participants and open-ended questions that can explain the occurrences.

For this research, a random sampling technique was chosen to include a variety of elderly from Thailand. This approach is relatively unbiased and allows one to examine the behaviour of different types of individuals. The main criteria of choice were the residency of the individuals and their age since this research specifically focuses on people aged 60 years old and older, resulting in 312 randomly chosen individuals, out of the 1,020,917 population of Bangkok and the sample size was chosen based on the computation.

The pilot study with 20 respondents was successful, indicating that no changes to the questionnaires, interviews, or other elements of research should be made to meet its objectives. This research is designed with Model 1 and Model 2 to have a clear distinction between the demographic characteristics of the sample population and the question relating to the smart home devices. Model 1 also includes some questions about the attitudes towards technology in living spaces. Model 2, in particular, allows the researcher to perform the analysis of statistical significance to evaluate the relationship between the variable and its dependables.

Sample size requirement In computing the required sample size in power analysis for Logistic regression test with Binomial distribution, G*Power software version 3.1.9.2 was used. Given two-tailed with an alpha (±) error probability of 0.05 and the power at 95% (1-² err prob) where the odds ratio was 2.33 calculated from 50% of the participants have a positive possibility in acquiring smart home technology, while the otherwise were assumed to be 30% of i.e.

Pr(Y=1|X=1) H1 = 0.5

Pr(Y=1|X=1) H0 = 0.3

To explore the dependency of the possibility that the elderly to acquire technology for their dwellings with the dependent variables extracted from two responses (yes/no) given the distribution of predictor (y_i) Bin(n_i, À_i) whether a trait is present (y_i=1), or not present (y_i=0).

The estimation of the covariation requires 312 of total samples size where Critical z was 1.96 and the actual power equals 0.95.

This research groups older adults by decade of age. It is also relative to data on the national statistics in older adults, so we use theSexagenarians (60-69 years old); Septuagenarian (70-79 years old); Octogenarian (80-89 years old).

Study areaThe researcher has piloted 20 sets of questionnaires to detect any overlooked errors as well as its comprehensive to the elderly visiting The Mall Bangkae department store during daytime to test its quality, comprehensibility, and validity. Since there is a considerable number of elderly aging at home in Thailand, we seek for collaboration from a domestic maid/helper service provider for elderly in Bangkok area to help on doing the informant questionnaire for us as well. +

The questionnaires were distributed to four different parts of Bangkok: Bang Khae district (West), Bang Rak district (Central), Lat Krabang district (East), and Lat Phrao district (North) where each part had one paid volunteer as an interviewer who would be interviewing participants. They were recruited and well-trained. Each of them was provided a questionnaires introductory instruction paper as they need to transcribe the questionnaires in a synchronous communication. All data collection is done by on site questionnaire survey i.e. interviews. There are no online questionnaires due to the complexity of the requirements of the research.

Data Collection

Present Data: Data was collected by the researcher during face-to-face meetings with the participants. The researcher collects data from a set of questionnaires that the researcher developed it to be a selective optimisation. A semi-formal interview conversation in respondent to the questionnaire. The setting was casual and extended in a conversational informant questionnaire. The researcher assisted in filling out the form using informant rated questionnaires. This is not only to offer convenience as well as casualness, but also to avoid the respondent limitation in their visual ability as the fonts on the form are quite small;

Secondary Data: Data collection from secondary data that was collected from a journal publication including related topic documents, research survey, and contents on the internet. The questionnaire paper contains two (2) parts in a front and a back of one paper:

Part 1) consisted of 34 variables and indicators items using a binary scale:

• Have you used or are you currently using this technology? (Yes=1/blank=0);
• Do you find it interesting to use? (Yes=1/blank=0);

The other section was a 5-likert scale that later collapsed into binary variables using the same grouping method: values 1 and 2 were combined to describe 0 = not important nor useful; value 3 means not relevant (tick or leave blank); and values 4 through 5 to describe 1 = important or useful. All blanks equal to 0. The converted analyses were:

• If yes, do you find this technology important or useful? (Yes=1/blank=0);
• If no, how positive do you perceive this technologys importance to your life? (Yes=1/blank=0)

Part 2) inquires household demographic characteristics such as total household size, age, gender, marital status, education and latest profession, household income, as well as type of house and form of house ownership.

Additionally, the researcher had created a 4:49-minute video comprising all situations cited in the questionnaire yet in an animation form. This video was aimed to be a supporting yet entertaining tool for the respondents to complete the questionnaire provided its story telling type that would potentially give a better understanding on the context. Lastly, the initial part 2 and 3 were combined and moved up to be on the front page so that the interview shall be started off with an informative entertainment that had effectively proven to shorten the total interview time comparing to the no-video session.

Data AnalysisStatistical AnalysisCompleted questionnaires were analysed using IBM SPSS Statistics programme version 23 with descriptive and inferential methods. 312 respondents are shown in Table 1. They comprised 51.9% of Sexagenarians age of 60-69, 32/1% of Septuagenarian age of 70-79 years old; and 16% of Octogenarian age 80 and above.

Descriptive StatisticsFrequencies, Mean, Percentage, Standard Deviation were used in order to describe the data statistically. 312 study participants descriptive characteristics are shown in Table 1.

Table 1. Descriptive Statistics of Socioeconomic Characteristics of participants (N=312).

With Exploratory Factor Analysis (EFA) that factors are analyse based on the correlation matrix in order to extract and group the similarity between each factor considering Factor Rotation and KMO and Bartletts Test that the identity matrix is not in a diagonal form. Testing the reliability of this questionnaire, the researcher considers Cronbachs Alpha Coefficient that this research refers to the role of thumb where the scale should not be lower than 0.7 (Hebert, 2010); (Karvonen et al., 2020); (Wahl et al., 2013) (Cronbachs Alpha according to all 25 variables processed was 0.7)

Inferential Statistics  Chosen Chi-square to test Independent factors. Descriptive framework and statistics of predictors were collaterally analysed.

Correlation was conducted to examine the relationship between X_k with Y (where the conditional probability of Y = 1 which is binary to indicate the strength of the empirical evidence of which predictors by the use of Logistic Regression model (Nunnally, 1978). Assuming that the model of the probability of the elderly acquiring the technology given a trait is:

À= success probability (always be between 0 and 1)

Y = binary response variable

X = Explanatory variables (predictor)

i = no. Explanatory variables

Table 2 Descriptive Statistics of Predictors by the Spatial frames (25 items).

Results and Discussion

Part one consisted of 34 variables and indicators items using a binary scale:

Have you used or are you currently using this technology? (Yes/No);

If yes, do you find this technology important or useful? (Yes/No);

If no, how positive do you perceive this technologys importance to your life? (Yes/No or leave blank if this is not relevant to ones life).

Given the responses collected in the questionnaire part 1, the researcher aggregated them to capture the positivity toward the smart home technology regardless of the item possession status of each individual. The possibility to acquire technology for their dwellings of the respondents (where Y =1) was the dependent variable and various potential predictors (X_k) and to estimate the parameters in regards of exploratory variable (x_i), all independent socioeconomic variables were univariately entry as well as into another two blocks:

• Block 1) Twelve (12) items of Socioeconomic factors; and
• block 2) Twenty-Five (25) items of Technology from the questionnaire.

With the Logistic Regression performed for the binary outcome measure and all 312 samples were undergone the maximum likelihood estimation (MLE) under mode Enter. Considered the odds scale that got multiplicative per every unit increase of exp(²₁), the covariant affected where

If ²_j > 0, then exp(²_j) > 1, and the odds increase.

If ²_j < 0,then exp(²_j) < 1, and the odds decrease.

Socioeconomic characteristics data (12 traits) and the components emerged from the standardized questionnaire (25 items) were analysed using logistic regression based on a nonlinear probability distribution (See Table 2). These two sets of explanatory variables as in correlated predictors analysed in the testing parameters were meant to predict the value of an observed dichotomous dependent variable regardless of the degree of freedom tests.

Table 3. Binary Logistic Regression Results: Attitude toward technologyPossibility that the Elderly to acquire technology for their dwellings.